In order to train deep learning models on 3D data to generalize to unseen examples, the dataset size should be in tens of thousands of samples [1]}, [2]}. However, current posed multi-view datasets are not suitable for leveraging the power of deep networks as can be seen in Table REF . The image resolution is either too low (\(64\times 64\)  in [3]}), the samples do not follow a controlled setup [4]}, [5]}, or lack diversity in the samples and classes [6]}. For these reasons, we construct a large and high-resolution dataset (SPARF) of posed multi-view images from ShapeNet [2]} that correspond to the same 13 classes originally used in the NMR dataset [3]}, but with an order of magnitude more images and pixels (17M 1M images and 400\(\times \) 400 64 \(\times \)  64 pixels). We also provide more than one million optimized sparse radiance fields of spherical harmonics and densities that allow for the novel view synthesis of the 40K models using Plenoxels [9]}.
